为了获取患者心血管内斑块特征的准确信息,并辅助临床医生对动脉粥样硬化区域进行判断和识别,文中进行了基于血管内超声(IVUS)图像的心血管粥样硬化斑块组织自动定征的研究.本研究收集了10个心血管疾病患者的IVUS图像,共207块斑块样本.首先,确定滑动邻域块的尺寸,令其中心像素遍历斑块区域,遍历过程中计算每个滑动邻域块的灰度均值和熵,并沿4个方向运用灰度共生矩阵法求出共生矩阵的10个局部特征;然后,对IVUS图像进行Gabor滤波和局部二值模式(LBP)处理,获得了更多的图像纹理特征;最后,通过线性分类器Liblinear、随机森林分类器(Random Forests)和调和最小值-广义学习向量量化分类器(H2M-GLVQ)对降维后的特征数据进行分类判决.将医生人工标记的结果作为金标准,自动定征的实验结果表明,随机森林和H2M-GLVQ分类器总体上对斑块组织的识别准确率均达到80％以上,其中随机森林分类器识别纤维化、脂质和钙化样本斑块的平均识别准确率分别为89.04％,80.23％和73.77％.%In order to obtain the accurate information of atherothrombotic plaques in the cardiovasculars and assist the diagnosis and classification of the plaque tissues,this study applied apply a machine learning method to automatically characterize the atherothrombotic plaques in intravascular ultrasound(IVUS) grayscale images.In this study,207 plaque samples in the IVUS images were collected from 10 patients with cardiovascular disease in the hospital.Firstly,the size of a sliding patch is determined and then its centre pixel traverses in the plaque area.The values of the mean and entropy are calculated.Ten features of the patch along 4 directions are respectively obtained by using co-occurrence matrix method.Secondly,more texture features of the plaque region in the IVUS images are obtained by using Gabor filter and local binary pattern(LBP) methods.Finally,the classifiers of Liblinear,random forests and Harmonic to Minimum-Generalized LVQ(H2M-GLVQ) are used to classify these pixels in the plaque tissues based on the features obtained through reducing dimension by using principal component analysis(PCA).The manual characterization by an experienced physician is considered as the gold standard.Results of the proposed automatic characterization method show the general identification rates of classifiers of random forests and H2M-GLVQ are over 80％.Compared with other two classifiers,the identification rate of random forests is relatively higher,i.e.89.04％,80.23％ and 73.77 ％ respectively for fibrotic,lipidic and calcified plaque tissues.
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